Diffusion tensor imaging of the normal-appearing deep gray matter in primary and secondary progressive multiple sclerosis

Abstract

Background

Despite strongly overlapping patterns of clinical and histopathologic findings in primary and secondary progressive multiple sclerosis, differences concerning motor symptoms, central nervous system inflammation, atrophy, and demyelination that cannot be accounted for by lesion load alone remain to be elucidated.

Purpose

To evaluate the normal-appearing deep gray matter in patients with primary and secondary progressive multiple sclerosis, diffusion tensor imaging was used in this study.

Material and Methods

In 14 multiple sclerosis patients with primary and secondary progressive multiple sclerosis, axial echo-planar single-shot diffusion tensor imaging sequences with 32 diffusion-encoding directions and axial FLAIR sequences were acquired on a 3T system using an eight-channel SENSE head coil. FLAIR hyperintense multiple sclerosis lesions were outlined semi-automatically and normal-appearing deep gray matter was outlined manually (caudate nucleus, globus pallidus, putamen, thalamus, substantia nigra, and red nucleus). Fractional anisotropy and mean diffusivity values within the normal-appearing deep gray matter for the two groups were compared.

Results

Interhemispheric differences in mean diffusivity values (but not in fractional anisotropy), were significantly higher in primary progressive multiple sclerosis than in secondary progressive multiple sclerosis for the substantia nigra (P = 0.04) and the putamen (P = 0.021). Volumes, mean diffusivity, or fractional anisotropy of the remaining normal-appearing deep gray matter did not differ significantly.

Conclusion

This study showed a higher interhemispheric difference in the mean diffusivity in the substantia nigra and putamen in patients with primary progressive multiple sclerosis than in those with secondary progressive multiple sclerosis. These changes may represent edema, as well as axonal and myelin loss that can affect the normal-appearing deep gray matter of the two hemispheres differently and may point to differences in the laterality of motor symptoms.

Keywords

Multiple sclerosis magnetic resonance imaging diffusion tensor imaging

Introduction

Multiple sclerosis (MS) is a complex T-cell dependent, inflammatory, demyelinating, and degenerative disease of the central nervous system (CNS) of which the precise pathogenesis is still unknown (1). With a prevalence of approximately 1 in 1000 in developed countries, it represents the most common cause of acquired neurological disability in young adults (2,3). MS has long been considered a disease that affects primarily the white matter (WM) of the CNS. However, a substantial number of studies have shown that gray matter (GM) is also involved in different stages of MS (4 –12). Signs of demyelination and axonal loss have been detected in WM, as well as in the cortical and subcortical deep GM of patients with different MS phenotypes on post-mortem studies, using advanced magnetic resonance imaging (MRI) analyses (9,11–18).

Different phenotypes of MS exist, with 80–85% of patients initially presenting with the relapsing remitting subtype (RRMS). Of these patients, 90% develop the secondary progressive subtype (SPMS) within 25 years (19). Primary progressive multiple sclerosis (PPMS) is characterized by a progressive course right from the onset and 15–20% of MS patients are diagnosed with this phenotype (20). Based on the clinical and histopathologic similarities between the different phenotypes of MS, it is currently thought that MS is one disease characterized by focal recurrent inﬂammation with or without relapses (21,22). However, clinical presentation and imaging features in PPMS and SPMS are not the same: PPMS and SPMS/RRMS differ clearly with regard to the age at the initial presentation of neurological symptoms and a lack of female preponderance in PPMS (23). At the time of onset, PPMS patients more frequently present with motor symptoms, ataxia, cerebellar, or brainstem symptoms than do SPMS patients (23). Pathological examinations indicate that the inﬂammatory reaction in the CNS is more prominent in SPMS than in PPMS (24). Gray matter atrophy is more pronounced in SPMS (e.g. in the left postcentral gyrus and bilateral thalami) (8). The extent of focal demyelinating brain lesions detectable on MRI (lesion load) is generally higher in SPMS than in PPMS (15).

Although numerous studies investigating different phenotypes of MS and normal controls have been undertaken (8,23–27), structural and functional CNS differences on MRI between patients with the two progressive phenotypes, PPMS and SPMS, have not been fully elucidated as yet, using standard or advanced imaging techniques. For example, a shift in hemispheric lateralization of functional connectivity has been reported in patients with MS together with changes in interhemispheric connectivity among homolog areas (28) with a majority of studies focusing on RRMS (29 –32) whereas a comparison of interhemispheric differences and changes in lateralization between progressive phenotypes has not been undertaken yet.

Diffusion tensor imaging (DTI) is an advanced MRI technique of growing interest for use in MS, as it allows the detection of abnormalities in areas of the CNS that appear normal on anatomical standard MRI sequences (normal-appearing GM [NAGM] and WM [NAGM]) (9,12,33 –37). DTI allows the study of the Brownian motion of water molecules impeded by cell membranes and myelin sheaths. Disruption of the myelin sheaths and/or axons, as occurs in MS, leads to increased motion of water molecules and, therefore, to increased mean diffusivity (MD). In addition, fractional anisotropy (FA) indicates the preferential motion of water molecules/protons along axons rather than perpendicular to them (38,39). MD has been shown to increase in the deep gray matter (DGM) in patients with different phenotypes of MS compared to healthy controls, while FA decreased in the thalamus and hippocampus and increased in the putamen and nucleus accumbens (9). However, differences between patients with different phenotypes have not been addressed as yet. Therefore, this study aimed to evaluate the global and interhemispheric differences between patients with PPMS and SPMS in DTI metrics of the normal-appearing deep gray matter (NADGM).

Material and Methods

The study was approved by the local ethical review board; written informed consent was obtained from all patients.

Patients

Fifteen patients with a diagnosis of MS, established according to the revised McDonald Criteria (40) either of primary or secondary progressive subtype, who did not suffer from any prior neurological, neurovascular, or psychiatric disease, head injury, or substance abuse were included in this prospective study between 2011 and 2012. All patients were identified consecutively by the treating clinicians. They were evaluated neurologically using the Expanded Disability Status Scale (EDSS) to quantify their disability status, with scores in the range of 0 (normal neurologic exam) to 10 (death due to MS) (41). The MRI was acquired within one week of the neurological examination. Demographics of the patients are presented in Table 1. The duration of MS was calculated from the patient records as the time difference between diagnosis of MS and acquisition of the study MRI. The duration of progression was calculated as the time difference between the diagnosis of progressive MS and the acquisition of the study MRI.

Table 1.

Patient demographics.

	SPMS		PPMS		All		P
Patients (n)	8		7		15
Sex (F/M) (n)	4/4		3/4		7/8		0.800
	Median	Range	Median	Range	Median	Range
EDSS	5.5	4–7	6.0	4–8	6.0	4–8	0.394
Age at MRI (years)	45.8	43.5–55.8	53.2	25.7–62.4	49.1	25.7–62.4	0.732
Age at onset of MS (years)	29.7	17.7–39.4	47.8	24.8–55.5	33.4	17.7–55.5	0.013
Age at onset of progression (years)	42.2	36.7–55.4	47.8	24.8–55.5	43.4	24.8–55.5	0.882
Duration of MS* (years)	18.7	10.1–29.7	5.4	1.0–9.3	10.1	1.0–29.7	0.001
Duration of progression^† (years)	2.9	0.4–10.1	5.4	1.0–9.3	5.1	0.4–10.1	0.577

Significant P values are in italics.

*Duration of MS = time difference between diagnosis of MS and acquisition of the study MRI.

^†Duration of progression = time difference between the diagnosis of progressive MS and the acquisition of the study MRI.

SPMS: secondary progressive multiple sclerosis; PPMS: primary progressive multiple sclerosis; MRI: magnetic resonance imaging; EDSS: expanded disability status scale; MS: multiple sclerosis.

Image acquisition

All MRI scans were acquired on a 3T scanner (Achieva, Philips Medical Systems, Best, The Netherlands) equipped with an eight-channel SENSE head coil. The following sequences were acquired in all patients:

FLAIR axial (field of view [FOV] = 336 × 240 mm; slice thickness = 4 mm; voxel size = 1.43 × 2 × 4 mm; TR = 11,000 ms; TE = 125 ms; TI = 2800 ms);

T2-TSE coronal (turbo spin echo) (FOV = 356 × 301 mm; slice thickness = 2.5 mm; voxel size = 1.57 × 1.86 × 2.5 mm; TR = 4500 ms; TE = 80 ms);

echo planar single shot DTI axial (FOV = 110 × 112 mm; slice thickness = 2 mm; voxel size = 1.75 × 1.75 × 2 mm; TR = 6495.6 ms; TE = 70 ms; b-values = 0 and 800 s/mm²; 32 diffusion encoding directions, including spectral fat saturation);

pre- and post-contrast T1-FFE (fast field echo) sequences sagittal (FOV = 240 × 240 mm; slice thickness = 1 mm; voxel size = 0.75 × 0.75 × 1 mm; TR = 8.26 ms; TE = 3.82 ms; flip angle = 8°), or pre and post-contrast T1 IR 2D axial (FOV = 400 × 319 mm; slice thickness = 4 mm; voxel size = 1.28 × 1.6 × 4 mm; TR = 3375 ms; TE = 15 ms; TI = 400 ms).

Image analysis

In all patients, lesions were identified on axial FLAIR sequences using the semi-automated segmentation tool, Jim (Version 5.0, Xinapse Systems, Northants, UK; http://www.xinapse.com), using standard settings. Lesion masks for each patient were created by transforming the lesion maps into images using Jim and the FMRIB Software Library (FSL; www.fmrib.ox.ac.uk/fsl) (Fig. 1, top row) to evaluate the lesion volumes per subject. In one patient with SPMS, co-registration errors in the processing of the ROI data outlining the MS lesions and the respective FLAIR sequence could not be overcome. Therefore, this patient was excluded from the analyses of MS lesions. In the remaining 14 patients, no such errors occurred.

The NADGM regions (caudate nucleus, globus pallidus, putamen, thalamus, substantia nigra, and red nucleus) were identified and outlined manually on all of each patient’s FLAIR images where the region was visible by a board-certified radiologist using MRIcron (www.mricron.com) (Fig. 1, center and bottom rows) (42).

Single-subject diffusion maps were processed using FMRIB’s Diffusion Toolbox (FDT), part of the FSL (43). Data were preprocessed using eddy current distortion correction, local modeling of diffusion parameters, and local fitting of diffusion tensors. FA and MD maps were extracted for each subject. The NADGM region maps and lesion maps were then registered to the diffusion maps’ space and FA and MD values within the NADGM regions and lesions were extracted for each subject to be submitted for further analysis. Registration and extraction were performed in the FSL. Interhemispheric differences in structures of the NADGM and lesions were calculated for FA and MD values for each patient and absolute interhemispheric differences were also calculated to use data regardless of laterality.

Statistical analysis

Statistical analyses were performed using the Statistical Package for the Social Sciences (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0, IBM, Corp., Armonk, NY, USA). Due to the small sample size, non-parametric testing with a Mann–Whitney U test was performed to compare parameters for both groups (PPMS and SPMS). Correlation coefficients were calculated using Spearman’s correlation.

Results

Fourteen patients with MS were included in this analysis, seven with SPMS and seven with PPMS. Nine patients were receiving disease-modifying treatment, including beta interferon (n = 2), glatiramer acetate (n = 1), natalizumab (n = 1), corticosteroids (n = 2), cyclophosphamide (n = 2), and mitoxantrone (n = 1).

Patients with PPMS and SPMS did not differ significantly regarding sex, EDSS, age, or duration of progressive disease. Age at onset of MS and duration of MS differed significantly between the two groups (P = 0.013 and P = 0.001), as was expected (Table 1).

One patient with SPMS was excluded from the analyses of MS lesions due to co-registration errors. The final two patient groups with PPMS (n = 7) and SPMS (n = 7) did not differ significantly regarding mean overall lesion volume (P = 0.728), or FA and MD within all lesions (FA_les and MD_les) (P = 0.247 and P = 0.563) (Table 2). Furthermore, there were no differences between PPMS and SPMS regarding the volumes of the NADGM or of single substructures (caudate, globus pallidus, putamen, red nucleus, substantia nigra, and thalamus) or for MD or FA of these structures (see Table 3 for P values). No enhancing lesions were found in our patients.

Table 2.

DTI metrics and lesion volumes of patients with SPMS and PPMS.

	All			SPMS			PPMS			SPMS vs. PPMS
	Median	IQR	P	Median	IQR	P	Median	IQR	P	P
Lesion volume (voxels)	4469	73,725		6591	12,637		3827	6386		0.728
Right hemisphere	2778	4427	1.000	3393	6389	1.000	2754	3001	1.000	0.187
Left hemisphere	2778	3481	1.000	3393	6248	1.000	2754	3433	1.000	0.643
MD_lesions	0.00110	0.00026		0.00114	0.00039		0.00109	0.00023		0.563
Right hemisphere	0.00107	0.00025	0.494	0.00109	0.00031	0.647	0.00104	0.00017	0.565	0.355
Left hemisphere	0.00113	0.00036	0.494	0.00118	0.00045	0.647	0.00110	0.00021	0.565	0.418
FA_lesions	0.30	0.04		0.29	0.03		0.31	0.03		0.247
Right hemisphere	0.31	0.06	0.419	0.31	0.06	0.248	0.31	0.06	0.848	0.908
Left hemisphere	0.30	0.04	0.419	0.29	0.04	0.248	0.32	0.02	0.848	0.049

Significant P values are in italics.

SPMSL: secondary progressive multiple sclerosis; PPMS: primary progressive multiple sclerosis; MD_lesions: mean diffusivity of lesions; FA_lesions: fractional anisotropy of lesions; IQR: interquartile range.

Table 3.

DTI metrics of the NADGM in SPMS and PPMS.

	MD					FA
	SPMS		PPMS		P	SPMS		PPMS		P
	Median	IQR	Median	IQR	P	Median	IQR	Median	IQR	P
NADGM	0.00087	0.00006	0.00091	0.00011	0.463	0.37	0.05	0.39	0.05	0.281
Bilateral
Caudate	0.00104	0.00023	0.00110	0.00025	0.397	0.27	0.11	0.27	0.09	1.000
Globus pallidus	0.00078	0.00008	0.00075	0.00008	0.397	0.44	0.04	0.43	0.09	0.613
Putamen	0.00080	0.00006	0.00080	0.00015	1.000	0.29	0.06	0.29	0.08	0.536
Red nucleus	0.00082	0.00010	0.00094	0.00015	0.152	0.43	0.08	0.43	0.07	0.613
Substantia nigra	0.00077	0.00009	0.00079	0.00011	0.536	0.50	0.05	0.54	0.12	0.536
Thalamus	0.00104	0.00019	0.00102	0.00032	0.867	0.32	0.05	0.34	0.01	0.121
Interhemispheric difference
Caudate	0.00019	0.00028	0.00054	0.00016	0.536	0.03	0.08	0.04	0.07	0.867
Globus pallidus	0.00003	0.00002	0.00003	0.00003	0.536	0.07	0.05	0.05	0.04	1.000
Putamen	0.00001	0.00002	0.00003	0.00004	0.021	0.04	0.05	0.03	0.11	0.694
Red nucleus	0.00011	0.00019	0.00016	0.00021	0.152	0.02	0.05	0.03	0.05	0.281
Substantia nigra	0.00002	0.00004	0.00007	0.00008	0.040	0.04	0.04	0.08	0.05	0.281
Thalamus	0.00009	0.00023	0.00006	0.00007	0.613	0.03	0.05	0.03	0.02	1.000

Significant P values are in italics.

MD: mean diffusivity; FA: fractional anisotropy; SPMS: secondary progressive multiple sclerosis; PPMS: primary progressive multiple sclerosis; NADGM: normal-appearing deep gray matter.

Interhemispheric differences of DTI metrics were evaluated among all patients and in the two groups of patients with PPMS and SPMS separately. No significant interhemispheric differences were observed regarding lesion volumes (P = 0.946; P_PPMS = 0.749; P_SPMS = 0.949). No significant differences between PPMS and SPMS regarding MD and FA within lesions were found except for one comparison: FA was significantly lower in lesions in the left hemisphere in patients with SPMS than PPMS (P = 0.049) (Table 2).

For the FA or MD values of the NADGM, no overall significant differences were observed between patients with PPMS and SPMS. However, absolute interhemispheric differences of MD values in the substantia nigra and the putamen were significantly higher in PPMS than in SPMS (P = 0.04 and P = 0.021, respectively), indicating a higher degree of asymmetry in PPMS. Absolute interhemispheric differences in FA or MD in the remaining structures of the NADGM were not significantly different between PPMS and SPMS (Table 3). Furthermore, the lesion volumes did not correlate significantly with FA or MD values of the NADGM (FA: P = 0.383; MD: P = 0.350).

Discussion

This is the first study to compare patients with PPMS and SPMS directly with regard to the potential differences in DTI metrics of the NADGM. Although overall DTI metrics of the NADGM were not significantly different between PPMS and SPMS, absolute interhemispheric differences in the DTI metrics of the substantia nigra and putamen were significantly higher in PPMS than SPMS, potentially indicating a higher degree of asymmetry in PPMS. The substantia nigra and the putamen play central roles in the regulation of motor behavior and are connected by the dopaminergic nigrostriatal pathway. The putamen is one of the structures receiving the majority of afferents to the basal ganglia whereas the substantia nigra is a major output structure of the basal ganglia (44,45).

The question of whether SPMS and PPMS are different manifestations of the exact same disease has not yet been fully elucidated. Despite many similarities on imaging, as well as on clinical and pathological examinations, some differences between the two progressive forms of MS have been repeatedly shown (23). This study set out to investigate the differences in the DTI metrics of the NADGM to further increase our knowledge about the two phenotypes.

A study that longitudinally evaluated MS patients, using MRI, showed a significant correlation of increasing iron accumulation in the substantia nigra, thalamus, and globus pallidus with disease severity (46). An ultrasound study revealed that the degree of substantia nigra changes could predict the two-year progression of neurological disability in MS, underlining the clinical significance of these changes (47). These results emphasize the importance of these DGM structures not only with regard to imaging characteristics, but also for clinical disease manifestation.

Among other differences between patients with PPMS and SPMS, it has previously been shown that PPMS patients more frequently present with motor symptoms and their GM atrophy differs from SPMS patients (8,23). One of the key roles of the DGM is motor control. Although, in our study, the overall volumes of the DGM did not differ between the two groups, we found significantly higher absolute interhemispheric differences in PPMS than in SPMS for the MD of the substantia nigra and the putamen. It has been shown that increased MD can reflect edema and a loss of axons and myelin (48,49), whereas decreased FA indicates demyelination (49,50). Generally, CNS structures, such as the substantia nigra and putamen, might be affected by MS due to demyelinating lesions, but also due to Wallerian degeneration where elevations in MD are usually preceded by a decrease in FA (51). The interhemispheric difference in MD, but not in FA, may be attributable to edema and axonal and myelin changes due to the slightly different effect multiple sclerosis may have on the substantia nigra and putamen of the two hemispheres. Functional connectivity changes in hemispheric lateralization have been shown mainly in patients with RRMS. Nevertheless, these connectivity changes might contribute to the interhemispheric differences observed here and have been shown to affect connectivity between different structures to different extents which may account for our findings in the substantia nigra and putamen but not other substructures of the NADGM (28 –32). Furthermore, it has been shown that, in lesional fibers in MS, simultaneous moderate elevations in axial and radial diffusivity may result in unchanged FA and altered MD (52), as were observed in the putamen and substantia nigra in our study. The inhomogeneity of the substantia nigra might also contribute to decreased sensitivity toward changes in FA (53).

Interhemispheric asymmetry has previously been studied, using DTI, in patients with RRMS and PPMS (54). In these patient groups, the interhemispheric differences found in DTI were higher in NAGM than NAWM and interhemispheric differences between the FA and MD of the thalamus, the caudate, and the centrum semiovale were more pronounced in patients with RRMS than with PPMS. The substantia nigra, however, was not evaluated in this previous study and no patients with SPMS were examined.

Regarding DTI metrics within MS lesions, the only significant difference between the two subgroups of patients in our study was lower FA in left hemispheric lesions in SPMS compared to PPMS whereas no significant differences between PPMS and SPMS were observed regarding MD. In patients with Parkinson’s disease, it has been shown that iron deposition is higher in the hemisphere that corresponds to the hemibody exhibiting the most severe symptoms (55). In our cohort, no correlation between the DTI metrics and EDSS could be found. A limitation of this study is that the clinical exam of our patients did not focus on identifying the sidedness of symptoms. However, closer investigation of interhemispheric differences using additional imaging modalities and their possible correlation with asymmetric symptoms may be of interest for further studies. In patients with PPMS, FA in left-sided lesions was slightly higher than in right-sided lesions, which was also accompanied by higher MD in left-sided lesions. It is worth noting that previous studies have shown in Wallerian degeneration, ageing, and Alzheimer’s disease, that combinations of micro- and macrostructural changes and closely related affected and unaffected WM tracts together with CSF contamination of voxels can cause this seemingly paradoxical relationship of the two metrics based on partial volume effects (56,57). However, none of these interhemispheric differences were significant (P = 0.848 and P = 0.565; Table 3).

Another limitation of this study is the number of patients included. Despite the small sample size, the two groups of patients, however, did not show any significant differences concerning EDSS, age at MRI, age at onset of progressive MS, or duration of progressive MS. Therefore, the differences between the two groups may point at differences between the two phenotypes of MS. Our results were not corrected for multiple comparisons due to the small sample size and the nature of the study being exploratory rather than confirmatory and should thus be interpreted with caution. However, this work might be a starting point for validating our results regarding interhemispheric differences in larger datasets. In our study cohort, motor symptoms were evaluated using the EDSS, but not separately for the two hemibodies. Thus, correlation of the DTI metrics with different symptom severities on the left and right side of the body remains to be evaluated in future studies. Furthermore, the NAWM was not evaluated; therefore, the FA and MD measured in lesions could not be compared to the surrounding NAWM. Furthermore, DTI metrics could only be compared among the two groups of patients but not to a third group of healthy controls which would have improved the interpretability further.

In conclusion, this is the first study to compare the DTI metrics of the NADGM of patients with PPMS and SPMS. We discovered a higher interhemispheric difference in the MD in the substantia nigra and putamen in patients with PPMS than in those with SPMS. Taking into account the results of previous studies, these changes may represent edema, as well as axonal and myelin loss that affect the two hemispheres differently and may point to differences in the laterality of motor symptoms which remain to be evaluated in future studies.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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